CN105599623A - System and method for predicting distance to empty of electric vehicle - Google Patents
System and method for predicting distance to empty of electric vehicle Download PDFInfo
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- CN105599623A CN105599623A CN201510680917.5A CN201510680917A CN105599623A CN 105599623 A CN105599623 A CN 105599623A CN 201510680917 A CN201510680917 A CN 201510680917A CN 105599623 A CN105599623 A CN 105599623A
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- energy efficiency
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- energy
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2250/00—Driver interactions
- B60L2250/18—Driver interactions by enquiring driving style
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/54—Energy consumption estimation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
A system and method for predicting distance to empty of an electric vehicle are provided. The method includes storing 1~n past mileages in a memory and calculating standard deviation for the 1~n past mileages. A mileage is then predicted by discriminately reflecting the 1~n past mileages based on the standard deviation. Accordingly, the distance to empty is calculated by multiplying the predicted mileage and battery available energy.
Description
Technical field
The present invention relates to the system and method for the driving range for predicting electric vehicle, andMore specifically, relate to the method for the driving range for predicting electric vehicle, it can pass throughUse standard deviation calculation calculate more accurately Initial energy source efficiency (specific energy consumption wheeled inNumber of passes) predict more accurately driving range.
Background technology
Conventionally, because battery can subtracting along with driving range (distancetoempty, DTE)Little and electric weight emptying, therefore during electric vehicle travels, DTE must as important driving informationMust offer driver by instrument bunch. The power consuming in motor, described electricityMotivation is the driving power source of electric vehicle, the power consumption causing because of operating air conditioning system etc.Etc. be used as the essential information of the residue driving range of predicting electric vehicle and toolDepend on the condition of travel body, also should consider the condition of up-hill journey or descent run.
Conventionally, can be by the driving range of following logic prediction electric vehicle.
Driving range [DET (km)]=energy efficiency [km/kWh] X battery utilisable energy[kWh]。
But, although use above logic to predict relatively accurately driving range, for electricityThe prediction of the initial driving range after the charging of pond, need to pay the utmost attention to energy efficiency (unitThe mileage number of energy consumption wheeled) Accurate Prediction.
Current, before the energy efficiency prediction after battery charging has mainly just been charged based on batteryEnergy efficiency draw, and after using the energy content of battery after charging and battery charging, institute is pre-The energy efficiency of surveying is calculated initial driving range. But, when calculating the feasible of electric vehicleSail apart from time, be to calculate by the information of energy efficiency before reflection only, and therefore work asWhen electric vehicle travels with the driving model before being different from just, initial driving range can go outNow larger error.
In other words, because the firm charging of battery driving model and battery afterwards just charged beforeDriving model difference, therefore larger error can occur in initial driving range. In other wordsSay, due to the energy efficiency of the driving model based on after just charging of battery with just fill based on batteryThe energy efficiency difference of the driving model before electricity, therefore initial driving range inevitablyThere is larger error.
In this part, disclosed above information is only for strengthening understanding to background of the present inventionObject, and therefore it may comprise and not form existing skill known to persons of ordinary skill in the artThe information of art.
Summary of the invention
The invention provides for by calculate more accurately driving range and more accurately predictionNecessary energy efficiency, in by the standard deviation that is stored in the passing energy efficiency in memoryCalculate the driving range after prediction battery charging, predict the driving range of electric vehicleMethod.
In one aspect, the invention provides the method for the driving range for predicting electric vehicle,It can comprise: in memory, store 1～n passing energy efficiency; Calculate 1～n passing energyThe standard deviation of source efficiency; By reflect distinctively 1～n passing energy effect based on standard deviationRate is predicted energy efficiency; And by making predicted energy efficiency and battery utilisable energy phaseTake advantage of to calculate driving range. Particularly, by reflecting distinctively 1～n based on standard deviationWhen individual passing energy efficiency is predicted energy efficiency, in the time that standard deviation is greater than by-level, canThe reflection ratio of the nearest energy efficiency by making 1～n passing energy efficiency minimizes to predict energySource efficiency.
Further, in the prediction of energy efficiency, energy efficiency can be predicted to be 1～n mistakeToward the mean value of energy efficiency. In addition, in the time that standard deviation is by-level, can be by making 1～nThe reflection ratio of the passing energy efficiency of individual passing energy efficiency minimizes and makes simultaneously nearest energy effectThe reflection ratio of rate maximizes to predict energy efficiency. In addition, when standard deviation is less than by-levelTime, the reflection ratio of passing energy efficiency that can be by making 1～n passing energy efficiency maximizesPrediction energy efficiency. In the time that accurate deviation is minimum of a value, energy efficiency can be predicted to be 1～n mistakeToward the mean value of energy efficiency.
Brief description of the drawings
Referring now to exemplary embodiment shown in the drawings describe in detail of the present invention above and itsHis feature, accompanying drawing only provides and does not therefore limit the present invention by the mode of explanation hereinafter,And wherein:
Fig. 1 illustrates according to an exemplary embodiment of the present invention for predicting the feasible of electric vehicleSail the flow chart of the method for distance;
Fig. 2 illustrates according to an exemplary embodiment of the present invention for predicting the feasible of electric vehicleSail the diagram of the map datum of the energy efficiency reflection ratio of the method for distance;
Fig. 3 A-Fig. 3 C is for illustrating as electronic for predicting according to an exemplary embodiment of the present inventionThe mark exemplary embodiment of the method for the driving range of vehicle, that use passing energy efficiencyThe diagram that the energy efficiency of accurate deviation is calculated; And
Fig. 4, for illustrating according to the present invention and existing methodical exemplary embodiment, uses by applicationIn the method for driving range of prediction electric vehicle, feasible after travelling on real roadSail the diagram of the sample calculation of distance.
It should be understood that not necessarily drafting in proportion of accompanying drawing, it is of the present invention basic that it presents explanationThe expression of simplifying to a certain extent of the various preferred embodiments of principle. As disclosed herein thisBright specific design feature, for example comprise concrete size, orientation, position and shape will part byThe application of particular desired and environment for use are determined. In the accompanying drawings, Reference numeral is in whole accompanying drawingIndicate of the present invention identical or be equal to part.
Detailed description of the invention
It should be understood that term as used herein " vehicle " or " vehicle " or other classComprise generally automotive like term, for example, comprise sports type multipurpose automobile (SUV)Passenger car, bus, truck, various commercial vehicle, comprise various ships and boats and shipsWaterborne vehicles, aircraft etc., and comprise that motor vehicle driven by mixed power, electric vehicle, plug-in type are mixedClose power electric vehicle, hydrogen-powered vehicle, and other alternative fuel vehicle (for example derives from stoneThe fuel of the resource outside oil). As referred to herein, motor vehicle driven by mixed power for have two kinds orThe vehicle of more kinds of power sources, for example, have petrol power and electric-powered vehicle simultaneously.
Although exemplary embodiment is described to use multiple unit to carry out example process, shouldUnderstand, also can carry out example process by one or more modules. In addition should understand,, term controller/control module refers to the hardware device that comprises memory and processor. DepositReservoir is configured to memory module and processor concrete configuration becomes to carry out described module, to carry outThe one or more processes that further describe below.
In addition, control logic of the present invention can be implemented as the nonvolatile on computer-readable mediumProperty computer readable medium, described computer-readable medium comprises by processor, controller/controlThe executable program instructions that unit etc. is carried out. The example of computer-readable medium comprises but does not limitIn ROM, RAM, CD (CD)-ROM, tape, floppy disc, flash memory disk driving, intelligenceCard and optical data storage. Computer readable recording medium storing program for performing also can be distributed in the calculating of networkingIn machine system, so that with distributed way storage and object computer readable medium, for example, logicalCross telematics server or controller local area network (CAN).
Term used herein, only for describing the object of specific embodiment, is not intended to restriction originallyInvention. Singulative used herein " one ", " one " and " being somebody's turn to do " are also intended to comprise multipleNumber form formula, unless context separately clearly states. Should further be appreciated that in this descriptionThe term using " comprises " and/or " including ", while use in this manual, refers to instituteThe existence of feature, integer, step, operation, element and/or the parts of stating, but do not get rid of oneDepositing of individual or multiple further features, integer, step, operation, element, parts and/or its setOr add. Term "and/or" used herein comprises in relevant Listed Items one or manyIndividual any and whole combination.
Unless specialized or apparent from context, term " about " used herein should be managedSeparate is for example, in the scope of normal tolerance in the art, in 2 standard deviations of mean value." approximately " can be understood as specified value 10%, 9%, 8%, 7%, 6%, 5%,4%, in 3%, 2%, 1%, 0.5%, 0.1%, 0.05% or 0.01%. Unless contextSeparately clearly state, otherwise all numerical value provided in this article is all modified by term " about ".
Hereinafter, will make detailed reference to various exemplary embodiments of the present invention, thisBright example is shown in the drawings and be described below. Although in connection with exemplary embodimentDescribe the present invention, but it should be understood that this description is not intended to that the present invention is limited to those and showsExample embodiment. On the contrary, the present invention is intended to not only cover exemplary embodiment, also covers and can wrapDraw together various modification in the spirit and scope of the present invention that are defined by the claims, amendment, etc.Jljl and other embodiment.
As mentioned above, can use " energy efficiency [km/kWh] X battery utilisable energy [kWh] "Calculate the driving range [DTE (km)] of electric vehicle, and for after battery chargingThe calculating of initial driving range, should pay the utmost attention to energy efficiency prediction more accurately.
Accompanying drawing 1 can for what predict electric vehicle for illustrating according to an exemplary embodiment of the present inventionThe flow chart of the method for operating range. Described method can be by having memory below hereinCarry out with the controller of processor. First, after the battery charging of electric vehicle (for example,After to the battery charging of vehicle), can in memory, store from travel starting point toThe passing energy efficiency of the end point of travelling. In other words, can in memory, store 1～n mistakeToward energy efficiency.
Then, can calculate the standard deviation of this 1～n passing energy efficiency. As everyone knows, markAccurate deviation (standarddeviation) is defined as the square root of the arithmetic mean of instantaneous value of square deviation. CauseThis, can obtain by following process the standard deviation of passing energy efficiency: obtain 1～n passingThe process of the mean value of energy efficiency, by each the deducting from 1～n passing energy efficiencyMean value obtains the process of each deviation; Ask the deviation of square value to ask flat to all deviations respectivelyThe process of side; The process of the square deviation summation to all square deviation summations; Sum of square of deviationsDivided by the process of asking variance of the number n of passing energy efficiency; And the variance obtaining is made evenThe process of root.
Further, can be by reflect distinctively 1～n passing energy efficiency based on standard deviationPredict energy efficiency, and can use particularly energy efficiency reflection map datum (mileageReflectingmapdata) prediction energy efficiency. For example, as shown in Figure 2, can be based on passingEnergy efficiency obtains standard deviation, and can reflect that map predicts energy effect by energy efficiencyRate, described energy efficiency reflection map by about the reflection ratio of nearest energy efficiency and based onThe experiment of the reflection ratio of the passing energy efficiency of the passing energy efficiency of standard deviation generates in advance(for example, formulism, editor etc.).
Predict energy effect by reflect distinctively 1～n passing energy efficiency based on standard deviationThe exemplary embodiment of rate is as follows: for example, when standard deviation is greater than (, about 1.5) by-levelFor example, when (, 1.0), can be by making nearest energy efficiency anti-of 1～n passing energy efficiencyThe rate of reflecting minimizes to predict energy efficiency.
In other words,, in the time that standard deviation is greater than by-level, can use energy efficiency reflection groundFigure determines Initial energy source efficiency, and described energy efficiency reflection map is mapped so that 1～n is individual passingThe reflection ratio of the nearest energy efficiency of energy efficiency minimizes. For example,, when standard deviation is quite highTime, energy efficiency can be confirmed as promptly changing, and this is because driver's nearest driving mouldFormula is different from driver's past driving model, at the beginning of can reflecting that map is determined from energy efficiency thusBeginning energy efficiency, wherein can make the reflection ratio of the nearest energy efficiency of 1～n passing energy efficiencyMinimize.
In addition, for example,, in the time that standard deviation is maximum (, 1.5 or larger), 1～n passingThe mean value of energy efficiency can be predicted as energy efficiency, as shown in Fig. 3 A. Further,In the time that standard deviation is by-level, can be by making the passing energy of 1～n passing energy efficiencyThe reflection ratio of efficiency minimizes and makes simultaneously the reflection ratio of nearest energy efficiency to maximize pre-Survey energy efficiency, as shown in Figure 3 B. In other words, in the time that standard deviation is by-level,Can be by the reflection ratio of passing energy efficiency of 1～n passing energy efficiency be minimized andMake the reflection ratio of nearest energy efficiency maximize from energy efficiency reflection map definite initial simultaneouslyEnergy efficiency.
Further, in the time that standard deviation is less than by-level (1.0), driver drives recentlyThe pattern of sailing can be confirmed as being similar to driver's past driving model, as shown in Fig. 3 C, borrowsThis can be by making 1～n passing energy efficiency the reflection ratio of passing energy efficiency maximize pre-Survey energy efficiency. In other words, in the time that standard deviation is less than by-level (1.0), driverNearest driving model can be confirmed as being similar to driver's past driving model, can lead to thusThe reflection ratio of crossing the passing energy efficiency that makes 1～n passing energy efficiency maximizes to imitate from the energyRate reflection map prediction Initial energy source efficiency.
In addition,, in the time that standard deviation is minimum of a value (0), energy efficiency can be predicted to be 1～nThe mean value of passing energy efficiency. Finally, can be by making the above-mentioned energy based on standard deviation predictionSource efficiency is multiplied by battery utilisable energy, calculates driving range, then can be by institute in instrument bunchThe driving range calculating is shown to driver.
As test case of the present invention, by applying respectively the method for existing calculating driving rangeMethod (the example of (for example, not application standard deviation) and calculating driving range of the present inventionAs, application standard deviation) measure driving range, wherein at air-conditioning system automatic operationUnder condition, travel preset distance (for example, being greater than 53km), wherein its result shown in Fig. 4.
As shown in Figure 4, according to existing method, than the actual value of initial driving range,The calculated value of initial driving range has 23% error. But, as shown in Figure 4, thisBright method shows, compares the actual value of initial driving range, the meter of initial driving rangeCalculation value only has 4% error, thereby showing that method of the present invention is compared existing method can be more accurateGround calculates driving range.
The present invention provides following effect by above-mentioned Technical Architecture. According to the present invention, can pass throughCalculating is stored in the standard deviation of the passing energy efficiency in memory and prediction is more accurately usedAccording to the energy efficiency of standard deviation calculation driving range, calculate more accurately wheeled distanceFrom.
Below describe the present invention in detail with reference to its exemplary embodiment. But, this area skillArt personnel will be appreciated that can be without departing from the principles and spirit of the present invention, to theseExemplary embodiment is made change, scope of the present invention enclose claim and they its etc.In effect equivalent, limit.
Claims (18)
1. for predicting the method for driving range for electric vehicle, comprise the following steps:
In memory, store 1～n passing energy efficiency by controller;
Calculate the standard deviation of described 1～n passing energy efficiency by described controller;
Reflect distinctively described 1～n passing energy by described controller based on described standard deviationSource efficiency is predicted energy efficiency; And
Make predicted energy efficiency and battery utilisable energy multiply each other to calculate by described controllerDriving range.
2. method according to claim 1, wherein, in the step of prediction energy efficiency,In the time that described standard deviation is greater than by-level, by making described 1～n passing energy efficiencyThe reflection ratio of energy efficiency minimizes to predict energy efficiency recently.
3. method according to claim 1, wherein, in the step of prediction energy efficiency,Energy efficiency is predicted to be the mean value of described 1～n passing energy efficiency.
4. method according to claim 1, wherein, in the step of prediction energy efficiency,In the time that described standard deviation is by-level, by making the mistake of described 1～n passing energy efficiencyMinimize and make simultaneously the reflection ratio of nearest energy efficiency to maximize toward the reflection ratio of energy efficiencyPrediction energy efficiency.
5. method according to claim 1, wherein, in the step of prediction energy efficiency,In the time that described standard deviation is less than by-level, by making described 1～n passing energy efficiencyThe reflection ratio of passing energy efficiency maximizes to predict energy efficiency.
6. method according to claim 1, wherein, in the step of prediction energy efficiency,In the time that described standard deviation is minimum of a value, energy efficiency is predicted to be described 1～n the passing energyThe mean value of efficiency.
7. for predicting the system of driving range for electric vehicle, comprising:
Be configured to the memory of stored program instruction; And
Be configured to carry out the processor of described programmed instruction, in the time being performed described in programmed instructionBe configured to:
In memory, store 1～n passing energy efficiency;
Calculate the standard deviation of described 1～n passing energy efficiency;
Come pre-by reflect distinctively described 1～n passing energy efficiency based on described standard deviationSurvey energy efficiency; And
By making predicted energy efficiency and battery utilisable energy multiply each other to calculate wheeled distanceFrom.
8. system according to claim 7, wherein, in the prediction of energy efficiency, whenWhen described standard deviation is greater than by-level, by making described 1～n passing energy efficiencyThe reflection ratio of nearly energy efficiency minimizes to predict energy efficiency.
9. system according to claim 7, wherein, in the prediction of energy efficiency, energySource efficiency is predicted to be the mean value of described 1～n passing energy efficiency.
10. system according to claim 7, wherein, in the prediction of energy efficiency, whenWhen described standard deviation is by-level, by making the passing of described 1～n passing energy efficiencyThe reflection ratio of energy efficiency minimizes and makes simultaneously the reflection ratio of nearest energy efficiency to maximize pre-Survey energy efficiency.
11. systems according to claim 7, wherein, in the prediction of energy efficiency, whenWhen described standard deviation is less than by-level, by making the mistake of described 1～n passing energy efficiencyReflection ratio toward energy efficiency maximizes to predict energy efficiency.
12. systems according to claim 7, wherein, in the prediction of energy efficiency, whenWhen described standard deviation is minimum of a value, described energy efficiency is predicted to be described 1～n passing energyThe mean value of source efficiency.
13. 1 kinds of nonvolatile computer-readables that comprise the programmed instruction of being carried out by controller are situated betweenMatter, described computer-readable medium comprises:
In memory, store the programmed instruction of 1～n passing energy efficiency;
Calculate the programmed instruction of the standard deviation of described 1～n passing energy efficiency;
Come pre-by reflect distinctively described 1～n passing energy efficiency based on described standard deviationSurvey the programmed instruction of energy efficiency; And
By making predicted energy efficiency and battery utilisable energy multiply each other to calculate driving rangeProgrammed instruction.
14. nonvolatile computer-readable mediums according to claim 13, wherein,In the prediction of energy efficiency, in the time that described standard deviation is greater than by-level, by making described 1～nThe reflection ratio of the nearest energy efficiency of individual passing energy efficiency minimizes to predict energy efficiency.
15. nonvolatile computer-readable mediums according to claim 13, wherein,In the prediction of energy efficiency, energy efficiency is predicted to be the flat of described 1～n passing energy efficiencyAverage.
16. nonvolatile computer-readable mediums according to claim 13, wherein,In the prediction of energy efficiency, in the time that described standard deviation is by-level, by making described 1～nThe reflection ratio of the passing energy efficiency of individual passing energy efficiency minimizes and makes simultaneously nearest energy effectThe reflection ratio of rate maximizes to predict energy efficiency.
17. nonvolatile computer-readable mediums according to claim 13, wherein,In the prediction of energy efficiency, in the time that described standard deviation is less than by-level, by making described 1～nThe reflection ratio of the passing energy efficiency of individual passing energy efficiency maximizes to predict energy efficiency.
18. nonvolatile computer-readable mediums according to claim 13, wherein,In the prediction of energy efficiency, in the time that described standard deviation is minimum of a value, described energy efficiency is by pre-Survey the mean value for described 1～n passing energy efficiency.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR10-2014-0158516 | 2014-11-14 | ||
| KR20140158516 | 2014-11-14 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105599623A true CN105599623A (en) | 2016-05-25 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201510680917.5A Pending CN105599623A (en) | 2014-11-14 | 2015-10-19 | System and method for predicting distance to empty of electric vehicle |
Country Status (3)
| Country | Link |
|---|---|
| US (1) | US20160137090A1 (en) |
| EP (1) | EP3020596A1 (en) |
| CN (1) | CN105599623A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111516553A (en) * | 2020-04-24 | 2020-08-11 | 东风汽车集团有限公司 | Method for calculating remaining endurance mileage of pure electric vehicle |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017125825A (en) * | 2016-01-15 | 2017-07-20 | 株式会社東芝 | Electric vehicle running supporting device, on-vehicle device, and running supporting method |
| CN107323279B (en) * | 2017-06-23 | 2019-08-02 | 北京新能源汽车股份有限公司 | Continual mileage modification method and device based on electric vehicle |
| US11043043B2 (en) | 2018-11-17 | 2021-06-22 | International Business Machines Corporation | Dynamic driving range prediction for electric vehicles |
| CN110222906A (en) * | 2019-06-17 | 2019-09-10 | 北京嘀嘀无限科技发展有限公司 | Electric vehicle energy consumption prediction technique, computer readable storage medium and electronic equipment |
| CN112277729B (en) * | 2019-12-31 | 2022-03-15 | 蜂巢能源科技有限公司 | Method and device for predicting total driving mileage of electric vehicle |
| CN114013285B (en) * | 2021-11-08 | 2023-11-21 | 北京理工新源信息科技有限公司 | Actual driving range evaluation method for electric automobile |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MX348341B (en) * | 2009-09-15 | 2017-06-06 | Kpit Cummins Infosystems Ltd * | Motor assistance for a hybrid vehicle based on predicted driving range. |
| US8629657B2 (en) * | 2009-12-31 | 2014-01-14 | Tesla Motors, Inc. | State of charge range |
| KR101936431B1 (en) * | 2012-03-20 | 2019-01-08 | 현대자동차주식회사 | DTE estimation method of electric vehicle |
-
2015
- 2015-09-28 US US14/867,782 patent/US20160137090A1/en not_active Abandoned
- 2015-10-12 EP EP15189355.9A patent/EP3020596A1/en not_active Withdrawn
- 2015-10-19 CN CN201510680917.5A patent/CN105599623A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111516553A (en) * | 2020-04-24 | 2020-08-11 | 东风汽车集团有限公司 | Method for calculating remaining endurance mileage of pure electric vehicle |
| CN111516553B (en) * | 2020-04-24 | 2021-12-17 | 东风汽车集团有限公司 | Method for calculating remaining endurance mileage of pure electric vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| US20160137090A1 (en) | 2016-05-19 |
| EP3020596A1 (en) | 2016-05-18 |
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Application publication date: 20160525 |